Find first true value in a row of Pandas dataframe

One way would be to use cumsum to help find the first:

In [123]: (b1 & b2 & (b2.cumsum(axis=1) == 1)).any(axis=1)
Out[123]: 
0     True
1     True
2    False
3    False
dtype: bool

This works because b2.cumsum(axis=1) gives us the cumulative number of Trues seen, and cases where that number is 1 and b2 itself is True must be the first one.

In [124]: b2.cumsum(axis=1)
Out[124]: 
   0  1  2  3  4
0  1  1  2  2  2
1  0  0  1  2  3
2  1  2  2  2  2
3  1  2  3  3  3

As a variation to @DSM's clever answer, this approach seemed a little more intuitive to me. The first part should be pretty self-explanatory, and the second part finds the first column number (w/ axis = 1) that is true for each dataframe and compares.

(b1.any(axis = 1) & (b1.idxmax(axis = 1) == b2.idxmax(axis = 1))

Worked out a solution which turned out to be similar to pshep123's solution.

# the part on the right of & is to check if the first True position in b1 matches the first True position in b2.

b1.any(1) & (b1.values.argmax(axis=1) == b2.values.argmax(axis=1))
Out[823]: 
0     True
1     True
2    False
3    False
dtype: bool